Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1073/pnas.2016887118 |
The diversity bonus in pooling local knowledge about complex problems | |
Payam Aminpour; Steven A. Gray; Alison Singer; Steven B. Scyphers; Antonie J. Jetter; Rebecca Jordan; Robert Murphy; Jonathan H. Grabowski | |
2021-02-02 | |
发表期刊 | Proceedings of the National Academy of Sciences |
出版年 | 2021 |
英文摘要 | Recently, theoreticians have hypothesized that diverse groups, as opposed to groups that are homogeneous, may have relative merits [S. E. Page, The Diversity Bonus (2019)]—all of which lead to more success in solving complex problems. As such, understanding complex, intertwined environmental and social issues may benefit from the integration of diverse types of local expertise. However, efforts to support this hypothesis have been frequently made through laboratory-based or computational experiments, and it is unclear whether these discoveries generalize to real-world complexities. To bridge this divide, we combine an Internet-based knowledge elicitation technique with theoretical principles of collective intelligence to design an experiment with local stakeholders. Using a case of striped bass fisheries in Massachusetts, we pool the local knowledge of resource stakeholders represented by graphical cognitive maps to produce a causal model of complex social-ecological interdependencies associated with fisheries ecosystems. Blinded reviews from a scientific expert panel revealed that the models of diverse groups outranked those from homogeneous groups. Evaluation via stochastic network analysis also indicated that a diverse group more adequately modeled complex feedbacks and interdependencies than homogeneous groups. We then used our data to run Monte Carlo experiments wherein the distributions of stakeholder-driven cognitive maps were randomly reproduced and virtual groups were generated. Random experiments also predicted that knowledge diversity improves group success, which was measured by benchmarking group models against an ecosystem-based fishery management model. We also highlight that diversity must be moderated through a proper aggregation process, leading to more complex yet parsimonious models. |
领域 | 资源环境 |
URL | 查看原文 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/313941 |
专题 | 资源环境科学 |
推荐引用方式 GB/T 7714 | Payam Aminpour,Steven A. Gray,Alison Singer,et al. The diversity bonus in pooling local knowledge about complex problems[J]. Proceedings of the National Academy of Sciences,2021. |
APA | Payam Aminpour.,Steven A. Gray.,Alison Singer.,Steven B. Scyphers.,Antonie J. Jetter.,...&Jonathan H. Grabowski.(2021).The diversity bonus in pooling local knowledge about complex problems.Proceedings of the National Academy of Sciences. |
MLA | Payam Aminpour,et al."The diversity bonus in pooling local knowledge about complex problems".Proceedings of the National Academy of Sciences (2021). |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论